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Spectrum sensing is a fundamental technology to detect the presence of primary user in cognitive radio networks. Usually, the requirements for spectrum sensing are very stringent. It requires that the spectrum sensing scheme has a good performance even in extremely low signal-to-noise ratio (SNR) environments. In this paper, we propose a novel spectrum sensing method based on stochastic resonance...
An optimal-stochastic-resonance-based spectrum sensing approach under color noise environment is proposed in this paper. It can improve the detection performance of energy detector in color noise background especially under low signal-to-noise radio (SNR). The suitable stochastic resonance (SR) noise and corresponding driving parameter can be introduced to maximize the output SNR of the SR system...
Spectrum sensing in cognitive radio becomes a challenging task when the signals received at the secondary users' transmitters exhibit low power. Locally optimum detectors (LOD) are therefore desirable thanks to their optimality in the low SNR regime. Here, we assume that the primary user transmits a training sequence, and propose a semi-blind LOD (SBLOD). In the case of BPSK signals, the test statistic...
Sensing the weak signals of primary users by cognitive devices greatly challenges the capability of energy detector under noise uncertainty. This paper proposes three schemes for detecting the primary signals of OFDM type. They are based on the auto-correlation ratio detection and exploit the multi-path diversity gain and multi-user cooperation. The detection and false alarm probabilities are then...
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